In the financial management industry, an increase in profit is typically realized when there is an increase in volume of credit contracts, a reduction in the loss from active credit contracts, or a combination of both. The major source of loss is typically due to customers defaulting in their payments. To reduce incidences of default in payments, it is important to carefully screen credit applications. Screening credit applications with stringent criteria can lead to significant reduction in losses due to defaults in payments. Such screening can help to identify when economic hardships are foreseeable from the applicant's financial status. However, stringent approval criteria may also result in a decline in the number of approvals of credit applications. Therefore, it is desirable to reduce the risk of loss without a significant impact on the growth in the volume of credit contracts. It is also desirable to increase the volume of credit contracts by controlling the criteria used to approve credit applications, without incurring a significant increase in the risk or loss or in actual loss.
Known techniques in controlling the criteria used to approve credit applications generally are based on the use of current spending data. However, in such techniques, the criteria are used to predict future economic conditions and spending based on data at a given point in time.
What is needed, therefore, is a credit management system that is more adaptable to real-time changes in the economy and spending habits.